This exploratory study investigates the rationale behind categorizing algorithmic controls, or algorithmic affordances, in the graphical user interfaces (GUIs) of recommender systems. Seven professionals from industry and academia took part in an open card sorting activity to analyze 45 cards with examples of algorithmic affordances in recommender systems’ GUIs. Their objective was to identify potential design patterns including features on which to base these patterns. Analyzing the group discussions revealed distinct thought processes and defining factors for design patterns that were shared by academic and industry partners. While the discussions were promising, they also demonstrated a varying degree of alignment between industry and academia when it came to labelling the identified categories. Since this workshop is part of the preparation for creating a design pattern library of algorithmic affordances, and since the library aims to be useful for both industry and research partners, further research into design patterns of algorithmic affordances, particularly in terms of labelling and description, is required in order to establish categories that resonate with all relevant parties
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Introduction: Sensor-feedback systems can be used to support people after stroke during independent practice of gait. The main aim of the study was to describe the user-centred approach to (re)design the user interface of the sensor feedback system “Stappy” for people after stroke, and share the deliverables and key observations from this process. Methods: The user-centred approach was structured around four phases (the discovery, definition, development and delivery phase) which were fundamental to the design process. Fifteen participants with cognitive and/or physical limitations participated (10 women, 2/3 older than 65). Prototypes were evaluated in multiple test rounds, consisting of 2–7 individual test sessions. Results: Seven deliverables were created: a list of design requirements, a personae, a user flow, a low-, medium- and high-fidelity prototype and the character “Stappy”. The first six deliverables were necessary tools to design the user interface, whereas the character was a solution resulting from this design process. Key observations related to “readability and contrast of visual information”, “understanding and remembering information”, “physical limitations” were confirmed by and “empathy” was additionally derived from the design process. Conclusions: The study offers a structured methodology resulting in deliverables and key observations, which can be used to (re)design meaningful user interfaces for people after stroke. Additionally, the study provides a technique that may promote “empathy” through the creation of the character Stappy. The description may provide guidance for health care professionals, researchers or designers in future user interface design projects in which existing products are redesigned for people after stroke.
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Algorithmic affordances are defined as user interaction mechanisms that allow users tangible control over AI algorithms, such as recommender systems. Designing such algorithmic affordances, including assessing their impact, is not straightforward and practitioners state that they lack resources to design adequately for interfaces of AI systems. This could be amended by creating a comprehensive pattern library of algorithmic affordances. This library should provide easy access to patterns, supported by live examples and research on their experiential impact and limitations of use. The Algorithmic Affordances in Recommender Interfaces workshop aimed to address key challenges related to building such a pattern library, including pattern identification features, a framework for systematic impact evaluation, and understanding the interaction between algorithmic affordances and their context of use, especially in education or with users with a low algorithmic literacy. Preliminary solutions were proposed for these challenges.
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The user’s experience with a recommender system is significantly shaped by the dynamics of user-algorithm interactions. These interactions are often evaluated using interaction qualities, such as controllability, trust, and autonomy, to gauge their impact. As part of our effort to systematically categorize these evaluations, we explored the suitability of the interaction qualities framework as proposed by Lenz, Dieffenbach and Hassenzahl. During this examination, we uncovered four challenges within the framework itself, and an additional external challenge. In studies examining the interaction between user control options and interaction qualities, interdependencies between concepts, inconsistent terminology, and the entity perspective (is it a user’s trust or a system’s trustworthiness) often hinder a systematic inventory of the findings. Additionally, our discussion underscored the crucial role of the decision context in evaluating the relation of algorithmic affordances and interaction qualities. We propose dimensions of decision contexts (such as ‘reversibility of the decision’, or ‘time pressure’). They could aid in establishing a systematic three-way relationship between context attributes, attributes of user control mechanisms, and experiential goals, and as such they warrant further research. In sum, while the interaction qualities framework serves as a foundational structure for organizing research on evaluating the impact of algorithmic affordances, challenges related to interdependencies and context-specific influences remain. These challenges necessitate further investigation and subsequent refinement and expansion of the framework.
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From the ACM record: "Software architecture reconstruction techniques may be used to understand and maintain software systems, especially in these cases where architectural documentation is outdated or missing. This paper presents the architecture reconstruction functionality of HUSACCT and describes how this functionality may be used and extended with algorithms in support of reconstruction research focusing on modular architectures. The tool provides a graphical user interface to select an algorithm, edit its parameters and to execute or reverse the algorithm. To study the results, browsers and diagrams are available. Furthermore, a user interface is provided to enhance the determination of the effectiveness of algorithms by means of the MoJoFM metric." https://doi.org/10.1145/3129790.3129819
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Background: Phantom limb pain is a frequent and persistent problem following amputation. Achieving sustainable favorable effects on phantom limb pain requires therapeutic interventions such as mirror therapy that target maladaptive neuroplastic changes in the central nervous system. Unfortunately, patients’ adherence to unsupervised exercises is generally poor and there is a need for effective strategies such as telerehabilitation to support long-term self-management of patients with phantom limb pain. Objective: The main aim of this study was to describe the user-centered approach that guided the design and development of a telerehabilitation platform for patients with phantom limb pain. We addressed 3 research questions: (1) Which requirements are defined by patients and therapists for the content and functions of a telerehabilitation platform and how can these requirements be prioritized to develop a first prototype of the platform? (2) How can the user interface of the telerehabilitation platform be designed so as to match the predefined critical user requirements and how can this interface be translated into a medium-fidelity prototype of the platform? (3) How do patients with phantom limb pain and their treating therapists judge the usability of the medium-fidelity prototype of the telerehabilitation platform in routine care and how can the platform be redesigned based on their feedback to achieve a high-fidelity prototype?
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‘Creating the Difference’ is the theme of the 2014 edition of the Chi Sparks conference. It is also the challenge that the Human-Computer Interaction (HCI) community is facing today. HCI is a creative field where practitioners engage in design, production, and evaluation of interactions between people and digital technology. Creating excellent interfaces for people, they make a difference in media and systems that people are eager to use. Usability and user experience are fundamental for achieving this, as are abilities at the forefront of technology, but key to a successful difference is getting the right concepts, addressing genuine, intrinsic, human needs. Researchers and practitioners contribute to this area from theory as well as practice by sharing, discussing, and demonstrating new ideas and developments. This is how HCI creates a difference for society, for individuals, businesses, education, and organizations. The difference that an interactive product or service makes might lie in the concept of it but also in the making, the creation of details and the realisation. It is through powerful concepts and exceptional quality of realisation that innovation is truly achieved. At the Chi Sparks 2014 conference, researchers and practitioners in the HCI community convene to share and discuss their efforts on researching and developing methods, techniques, products, and services that enable people to have better interactions with systems and other people. The conference is hosted at The Hague University of Applied Sciences, and proudly built upon the previous conferences in Arnhem (2011) and Leiden (2009). Copyright van de individuele papers ligt bij de betreffende auteurs.
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This guide was developed for designers and developers of AI systems, with the goal of ensuring that these systems are sufficiently explainable. Sufficient here means that it meets the legal requirements from AI Act and GDPR and that users can use the system properly. Explainability of decisions is an important requirement in many systems and even an important principle for AI systems [HLEG19]. In many AI systems, explainability is not self-evident. AI researchers expect that the challenge of making AI explainable will only increase. For one thing, this comes from the applications: AI will be used more and more often, for larger and more sensitive decisions. On the other hand, organizations are making better and better models, for example, by using more different inputs. With more complex AI models, it is often less clear how a decision was made. Organizations that will deploy AI must take into account users' need for explanations. Systems that use AI should be designed to provide the user with appropriate explanations. In this guide, we first explain the legal requirements for explainability of AI systems. These come from the GDPR and the AI Act. Next, we explain how AI is used in the financial sector and elaborate on one problem in detail. For this problem, we then show how the user interface can be modified to make the AI explainable. These designs serve as prototypical examples that can be adapted to new problems. This guidance is based on explainability of AI systems for the financial sector. However, the advice can also be used in other sectors.
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In flexible education, recommender systems that support course selection, are considered a viable means to help students in making informed course selections, especially where curricula offer greater flexibility. However, these recommender systems present both potential benefits and looming risks, such as overdependence on technology, biased recommendations, and privacy issues. User control mechanisms in recommender interfaces (or algorithmic affordances) might offer options to address those risks, but they have not been systematically studied yet. This paper presents the outcomes of a design session conducted during the INTERACT23 workshop on Algorithmic Affordances in Recommender Interfaces. This design session yielded insights in how the design of an interface, and specifically the algorithmic affordances in these interfaces, may address the ethical risks and dilemmas of using a recommender in such an impactful context by potentially vulnerable users. Through design and reflection, we discovered a host of design ideas for the interface of a flexible education interface, that can serve as conversation starters for practitioners implementing flexible education. More research is needed to explore these design directions and to gain insights on how they can help to approximate more ethically operating recommender systems.
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Purpose: The purposes of this study were, first, to (re)design the user-interface of the activity tracker known as the MOX with the help of input from elderly individuals living independently and, second, to assess the use of and experiences with the adapted Measure It Super Simple (MISS) activity tracker in daily life. Methods: The double diamond method, which was used to (re)design the user-interface, consists of four phases: discover, define, develop, and deliver. As a departure point, this study used a list of general design requirements that facilitate the development of technology for the elderly. Usage and experiences were assessed through interviews after elderly individuals had used the activity tracker for 2 weeks. Results: In co-creation with thirty-five elderly individuals (65 to 89-years-old) the design, feedback system, and application were further developed into a user-friendly interface: the Measure It Super Simple (MISS) activity. Twenty-eight elderly individuals (65 to 78-years-old) reported that they found the MISS activity easy to use, needed limited help when setting the tracker up, and required limited assistance when using it during their daily lives. Conclusions: This study offers a generic structured methodology and a list of design requirements to adapt the interface of an existing activity tracker consistent with the skills and needs of the elderly. The MISS activity seemed to be successfully (re)designed, like the elderly who participated in this pilot study reported that anyone should be able to use it.
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